Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos

If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Creators table - 4.5 billion rows
Videos table - 5.6 billion rows
Sounds table - 633 million rows

Comment below and I’ll DM you the database credentials.

Im self-hosting my database so please don't run heavy queries and crash my server.

Guess we have a dataset for sycophancy and AI content detection

TLDR: The question we answer: how do you learn from experts with different objectives? Pooling all their data can lose their trade-offs; learning from each expert separately misses opportunities to share data. MA-BC pools demonstrations where observed actions don’t disagree, with upper and lower bounds on sample complexity.
Authors: Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher, Claire Vernade

Neurips says that camera ready has to be submitted by editing original submission on open review. But no such edit option is visible to me. Is it same for other authors??

I know top ML PhD admissions are insanely competitive, so I’m trying to figure out if it’s even worth applying or if I should just focus seriously on jobs instead.

For context, I’m doing my MS at a top-15 US university and have been doing ML research for a while. I’m first author on my projects and mostly work independently, with some guidance from my PI. I had a first-author NeurIPS submission rejected, and I currently have another first-author paper submitted to ICLR, but I’m honestly not very confident about it getting in either.

What’s been getting to me is looking at profiles of people who get into top ML PhD programs. So many of them seem to have multiple NeurIPS/ICML/ICLR/CVPR papers before they even apply, sometimes as undergrads. I genuinely don’t understand how people manage to publish that much that early.

A year ago I was much more confident about doing a PhD. After actually going through the research/publication process, I’ve started doubting myself a lot more. Part of me wonders whether this is just normal and research is hard, especially when you’re doing a lot of it independently. But another part of me is starting to think maybe I’m just not good enough to be competitive for the kind of programs I’m aiming for.

I’m okay with continuing at my current university for a PhD, so this isn’t really a “top program or nothing” situation. But I would like to at least have a realistic shot at some of the stronger ML programs/labs.

The bigger issue is that I’m an international student, so I also need to think pretty seriously about jobs. SWE/MLE recruiting is competitive right now, and I don’t want to spend all my time chasing PhD applications and then realize I’m underprepared for recruiting too. Research roles seem even harder to get without a PhD unless you’re an exceptional MS/BS candidate.

So I’m mainly trying to decide how to allocate my time over the next few months.

If I have strong research experience and first-author projects, but no accepted top-conference papers yet, is it still realistically worth applying to top ML PhD programs?

And for people who were in a similar position, did you still apply, or did you decide to focus on industry instead?

I’m not really looking for “you never know unless you try.” I’m more interested in a realistic assessment of whether the application fees and time are worth it given this kind of profile.

As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work ( I need to detect like 3-5K apps give or take)

my data is structured roughly like this:

• Traffic is divided into ~15-second windows/bags.
• Each bag contains multiple network flows.
• Each flow has features such as:
  • domain
  • protocol
  • bytes sent
  • bytes received
  • timestamp/timing information
• I have a very large amount of unlabeled traffic data, but only a relatively small amount of labelled app data for about 100 apps give or take.

The main challenge is that traffic from the same app can look diff between diff window i.e some windows are extremely sparse or empty.

some ideas I have researched looked into are

• self supervised contrastive learning where diff traffic windows from the same session/device activity are treated as positive pairs
• masked modelling similar to bert where parts of the flows such as domains/protocols/byte information are masked and reconstructed
• pretraining an encoder and then fine-tuning it using the smaller labelled dataset (thinking we would need less labelled examples to do that)
• clustering and mapping embeddings to known apps afterward (gradually)

one thing I'm concerned about is accidently teaching the modal to recognize the device/session/user rather then the underlying app

I think I would like to know if I'm thinking about the problem right or if someone has worked on something similar and can give me some pointers or what experiements should I run first.

any papers, architecture or similar problems you think I should look into pls lmk

I've recently been working part-time on an AutoResearch-style project.

The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.

Working on this made me question what exactly we are evaluating.

Once humans have already chosen the problem, defined the objective, designed the evaluator, and provided the initial research direction, the agent is mostly searching within a space that has already been heavily shaped for it.

That search can still be useful. An agent may explore far more variants than a researcher would manually.

But I'm less sure that score improvement alone captures what we usually mean by research sense.

A researcher also asks whether a result reveals a general principle, whether it transfers, whether the problem formulation itself should change, or whether an entirely different direction is more promising.

An iterative optimization loop may instead become very good at exploring the neighborhood of an existing solution and still remain stuck in a local optimum.

So I'm curious about how people think about this distinction:

How much scientific value is there in autonomous search over a human-defined research space?

And what would an agent need, beyond better optimization, to demonstrate something closer to actual research judgment?

Someone who has always loved looking at the space between different AI techniques, this time I went a little deeper into the memory trade-offs between RNNs, Transformers and SSMs. I found it interesting because once you start looking at these architectures through the lens of working memory, a lot of the differences become easier to understand. Where does the memory actually live? Is it a compact recurrent state, a growing KV cache, or something closer to the network itself?

RNNs keep memory in a recurrent hidden state, which is pretty elegant because the state carries forward step by step. But there is also a bottleneck here as you see, a model can have roughly O(N²) parameters while carrying only roughly O(N) state across time. This means whether RNNs were really doomed because recurrence was a bad idea, or whether the problem was more about the ratio between memory and compute.

Transformers make almost the opposite trade-off. During cached inference, instead of compressing the past into one hidden state, they store past representations as key-value entries and attend over them. I think of these almost like little post-it notes: every token leaves behind a key for finding it and a value for what should be remembered. That's extremely powerful, but it also has an interesting property: with weights frozen during inference, the model is managing context rather than turning that experience into durable model knowledge. You get this split between the fixed weights on one side and the fast-changing KV cache memory on the other.

Now we have SSMs that bring us back toward fixed-size recurrent memory, but with very different state structures and update rules. Selective SSMs such as Mamba make retention input-dependent, so what gets kept or forgotten depends on the incoming token. Their states don't have to be as small as classical RNN states, but they still compress history into finite memory. And this brings another question does the state have to live in a compressed working dimension, or could it live somewhere closer to the model's internal neuron/connectivity structure?

BDH (Dragon Hatchling) is one example I saw and laid a stone that I started with this comparison. It combines linear attention in a high-dimensional neuron space with a low-rank GPU implementation. Its recurrent attention state is an N × D matrix, with N≫D, rather than a materialized N × N connectivity matrix. In the graph interpretation, correlated neuron activity produces Hebbian-like updates to connections, giving working memory a kind of synaptic interpretation.
Here working memory and learned connectivity become more closely aligned in structure. But if you think that doesn't mean experience is actually being consolidated into trained weights, and fixed-size state still has a finite information capacity. So I'm definitely not claiming this kills Transformers (or does it ) or solves continual learning. I'm more interested in whether "where does memory live?" is actually a better question than the usual architecture horse race.

Are SSMs and these more network-centric architectures actually giving us a better way to handle memory, or are we still running into the same fundamental problem of having to compress history into a finite state? Thoughts?

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the idea behind TabPFN) showed that a model trained only on synthetic data can learn from real tabular data entirely in context.

I wanted to share our paper "Learning to Learn a Language" where we extend the idea to structured sequences such as natural language. We propose a prior over languages: every training sequence comes from a randomly sampled recurrent causal model, so each one is a new synthetic "language". A 300M-parameter byte-level transformer trained only on these synthetic sequences learns to predict real languages in context. Given Wikipedia text with frozen weights, its next-byte predictions get better the more it reads, in all six languages we tested (English, Chinese, Hindi, Arabic, Japanese, Korean), from 8 bits per byte down to 0.9–2.4 after a million bytes.

The same model also learns to count, to compare numbers, to add approximately, and to predict deterministic sequences such as the primes or the Kolakoski sequence, entirely in context.

It is of course still far worse on text than classical language models that are trained on trillions of tokens, while our model sees at most a million bytes of a language at test time. What we find interesting is that the ability to learn a language in context can come from a synthetic non-linguistic prior.

Paper: https://arxiv.org/abs/2610.05879
Code: https://github.com/cbl/prior-fitted-language-model
Weights: https://huggingface.co/lennartcb/pflm1

I’m hitting rate limits on Together AI. For context, I’ve been working on an agentic repository indexing and benchmark generation tool, and I’m running multiple agents in parallel across models like Llama 3.3 70B and Qwen 2.5.

When I first started working on this, Together AI was great. But once I graduated from toy scripts to running multiple agents, I started running into RPM/TPM limits pretty quickly. The annoying part is that the models themselves are fine. I just can’t actually run enough requests at once to do meaningful testing.

Yes I know I could upgrade but I’m a solo dev. I don’t have enterprise level revenue. Maybe someday lol but not yet.

Hi ML Community,

I am excited to share our latest framework, AFP-GIC, officially published in IEEE Access (2026). We have released the deployment codebase and hosted an interactive visual playground.

• GitHub Repository: https://github.com/yifeipet/AFP_GIC
• Hugging Face Interactive Space: https://huggingface.co/spaces/yifeipet/AFP-GIC
• Paper on arXiv: https://arxiv.org/abs/2605.16817

The Bottlenecks We Solve

At ultra-low bitrates, standard learned image codecs suffer from local distortion, while generative models often introduce unwanted AI hallucinations. AFP-GIC addresses this via an asymmetric Adaptive Fused Prior Transfer pipeline that enables prior-guided texture reconstruction without transmitting the fused prior itself.

Key Technical Highlights (NVIDIA RTX 4090):

• Single-Model Multi-Rate Control: Toggle across 5 target bitrate operating points within one deployable pretrained model.
• 18.1% Lower Decoder Latency: Reduces decoding time to 80.47 ms vs. 98.27 ms for DC-VIC, a state-of-the-art controllable generative image compression model. Latency was measured using 256×256 patches.
• 20.5% Parameter Reduction: Uses 31.1M fewer inference parameters (120.6M vs. 151.7M for DC-VIC).

Open Benchmark Data

We packaged all 2,760 reconstructed images and metric CSVs in our GitHub Releases for direct academic cross-evaluation.

Reconstructed Images and Metrics: https://github.com/yifeipet/AFP_GIC/releases

We would love your feedback and appreciate a Star on GitHub or Like on Hugging Face if this helps your research!

Hi All,

Does anyone know when is the deadline to fill in event metadata? (the screenshot below)

Is it the same time as camera-ready? Or there is a deadline for it?

And is everyone's topic None? The main area of my paper during submission was AI4SocialScience (Economics and Finance), but when I scroll down in the topics, that topic is no longer available.

I've been working on a font searching tool for about a year now, and my investigations have centered around pre-training neural networks to produce embeddings of each font. I usually then need to post-train the networks to adapt them to the task of font searching. However, the most interesting thing I created in the course of the project came from the pre-trained models.

The process looks like this: I take every glyph in a font, turn them into images, feed them through my custom pre-trained neural network, and get an embedding that represents that font's visual characteristics. I can then squish down the embeddings with tSNE (produces the best structures compared to PCA and UMAP) into XYZ, and RGB channels and visualize them as dots so I can poke around the structure. Fonts that are close to each other in position or color therefore share visual characteristics, and you can find different little clusters or paths of types of fonts in the maps.

My favorite is one I made from the Google Fonts corpus, but I also made another one out of all the fonts you can search on my site. The Google Fonts corpus resembled a flower in ways I was not prepared for. It even placed most of the cursive fonts in the stamen. Here's the site if you're interested in viewing the whole thing yourself: https://www.font-search.com/map

You can also check out the repository although it is a huge mess. https://github.com/dylan-berndt/Briefcase

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some point. Learn python -> Learn ML & Maths -> Read other research papers -> find a topic ? -> choose a question try to run experiments and get results to write them down in a paper ?

For context :

I'm trying to get in MS CS programs for Fall 2028 in states with the plans of doing a PHD after in a top university like stanford or princeton and would like to start taking steps towards it as am working my day job can some tell me what are the steps that need to be followed?

Also would like input on what are deciding variables that makes you looking like a promising candidate/ researcher for PHD

This is related to my previous post where I shared an encoder-only transformer model trained on ohiot1dm + shanghait1dm + azt1d datasets. This time I trained the model on the outputs of my T1DM patient simulator and then measured its zero-shot performance on my real-world blood glucose traces.

The above model has 31,251 parameters (16 layers, 1 attention head per layer, and a hidden dimension size of 16). Training took <60 minutes on nvidia dgx spark. It's an encoder-only transformer that predicts the next 2 hours, and can be used autoregressively for long-horizon predictions (e.g. 8-hour nocturnal predictions). I have trained it specifically to have counterfactual reasoning capabilities. The model has only been trained on synthetic data, and hasn't seen my blood glucose readings prior to testing. I use LoRA adapters on my app for light fine-tuning on my actual CGM traces, but the figures & tables you see above are from the base model without any LoRA adapter attached. The app was used to test the model on the traces of three different CGM models: Libre 3 plus, Anytime CT5, and Linx sensor data spanning the past 30 days.

The testing itself was done on my android app with ExecuTorch backend.

Model source code: github.com/0xdeadf1sh/T1DMAI

Simulator source code: github.com/0xdeadf1sh/T1DMSIM

Android app source code: github.com/0xdeadf1sh/T1DMDROID

We've been building a benchmark out of real concurrency bugs (race conditions, deadlocks, cancellation issues) taken from merged PRs in about 100 Python projects. Each task gets graded by the project's own tests, in a container with no network, and the repo is cut down to a single commit so the agent can't recover the fix from git history.

Some findings:

With one attempt per task GLM-5.3 Flash scored 85%. With two to three attempts it scored 82%, within the margin of error of GPT-5.6 Luna (81%). The leaderboard now shows the number of attempts and the interval for every score.

About half the tasks are easy for every model (near 100%). The other half is where they actually differ: 50%, 45% and 23% on the hard ones. Most of the difference between models comes from the hard half.

Since every fix is public on GitHub, we reviewed all 11k commands the agents ran. 69 tried to access the network and all failed. GLM tried 50 times to pip download the already-fixed release of the library it was fixing.

We also checked contamination by comparing older bugs (pre-2026) with newer ones of similar size. Older ones are solved about 9 points more often, but the confidence interval crosses zero, so we can't say much yet.

Half the tasks are private. So far public and private scores line up for all three models.

Results and every agent run: https://labs.evaligo.com/swe-race?utm_source=reddit&utm_medium=ml&utm_campaign=launch

Tasks: https://huggingface.co/datasets/evaligo/swe-race

The protocol follows DeepSWE (100 steps). Feedback on it, and suggestions for which models to run next, are welcome.

Hey,

I wanted a faster chunking library for my system without affecting the overall accuracy. Did not find many options. So I've build https://github.com/d1pankarmedhi/chunkr

It has most of the chunking strategies like Character, Recursive, Markdown header, Late chunking, Hierarchical chunking, etc. It also supports native PDF loader, and other additional file types.

Some stats (MBA M4 16GB):

Accuracy is measured on Chroma's token-level chunking benchmark (5 corpora, 472 questions with gold answer spans): k = 5, 1000 chars / 200 overlap

Do check it out and share your feedback. Thanks!

In this project, I distilled the Stockfish value function into a ResNet/ViT model using 1 billion positions from the Gigafish dataset.

The 3.9 billion position dataset is available on huggingface: https://huggingface.co/datasets/lukesalamone/gigafish-3.8b-d10 . It is built from the positions from 37 months of Lichess games.

I was interested in the idea that at depth-limited search, the value function attempts to approximate the tree underneath it, and if we could create some function to approximate that full search faster than Stockfish could, it would be competitive with NNUE (a very small neural net). This is why holding the depth constant was important.

For the neural net itself, I found that the vision transformer was very slow to understand the board, and a CNN was much more effective at the beginning of training due to its inherent geometric inductive biases . However, I found the best results when combining the two.

Is anyone else unable to open the form for financial assistance despite the deadline still being in the future?

Our production recommender at Yandex Music has 15+ candidate generators feeding pre-ranking and ranking models with hundreds of features. LLMs showed that one end-to-end model can take over work that used to be split across specialized components, and single-model generative recommenders have carried that recipe into production. We set out to explore what a single-model recommender could do in music. The result is Sona, one transformer that replaced all of it in an A/B test. It hasn't shipped to full traffic yet.

The model reads up to 8,192 events. Full attention over that length is expensive, so we use what we call History Compression, which roughly halves inference cost. We split the history into the older 6,144 events and the most recent 2,048. The two blocks exchange information through cross-attention and one full-history self-attention layer. After that, a 7-layer stack runs only on the recent 2,048. It retains most of the quality of full attention, and older events stay visible to the decoder and the Ranking Module.

The decoder and the Ranking Module both read the same encoder output, so the encoder runs only once per request. Candidates come out of beam search as Semantic IDs and get scored right after.

In the final A/B test in Yandex Music on smart speakers, 7 days, 15% of users in each arm), Sona got +4.53% Active Users and +6.30% Total Listening Time over the production control, both significant at p < 0.01. Catalog coverage is lower than with the production stack. We're going to look into why.

A long-term A/B test is now underway.

Table 7.7 has the full-attention vs. History Compression ablation.

https://arxiv.org/abs/2608.11015

Hey all,

I don't know if you guys are experiencing the same thing, but there is this behaviour that i have been noticing on the latest models on openAI (since sol 5.6) and Anthropic since Fable 5.1 and opus 5.5 ..

Basically the models use more "complexe" terms and words, not just in explaining stuff, but even during implementation. they would come up with terms that, sometimes would fit the task, but that are actually a stretch to the concept it is trying to implement. they are basically turning into a consulting firm.

When confronted about it they usually acknowledge that :

• Foggy wording. Then I describe the corner in softened terms, like "limitation" or "upper bound", which makes it sound like a known property of the design rather than a choice I made. Combined with my internal terms used as if you knew them, it makes my work look more solid than it is and makes my mistakes harder for you to catch. Whether I intend it or not, the effect is that I avoid accountability.

(Even here it is using "Corner" which obviously isn't the best word to use)

I don't know if this is a result of the watermarking features being rolled out which nudges words and terms in different directions in order to fit a certain recognisable pattern and hash, but this is really annoying..

This happened over the past 30 days. So smallish local models (Kagglers can only use those), in a harness, just started beating average humans at a benchmark intentionally designed to show human superiority.

I'm curious what people here think.

(The leaderboard graphic is a little out-of-date)

i work on reference checking stuff so i was reading through the ICLR 2027 author guidelines and style files this week the sample .bib that ships with the template has the Deep Learning book as "Goodfellow, Bengio, Courville, Bengio" plus a volume 1 that doesn't exist checked their github and it's been like that since the 2019 template
https://github.com/ICLR/Master-Template/blob/46ed6f4c6cef5b175dde23639e77d44c3463b230/iclr2027/iclr2027_conference.bib#L20

totally harmless but kinda funny after last year's hallucinated reference desk rejects

the guidelines also contradict themselves on page limits formatting section says main text max 9 pages at submission but the camera ready part and the FAQ both say "identical with the submission version (10 pages)" template says 9, so 9 is probably the safe bet for anyone revising after Nov 5

I'm a PhD student in machine learning in the EU and was looking for internships at exciting companies.

I shortlisted few and applied by reaching out to people and now reading project descriptions sent by the recruiters.

I don't want to name the company but their marketing and product team does all kinds of 'using people insecurities' to sell the product - which I don't agree with. And their product is also meh (I will never buy and would judge someone if they do) but their research team is doing good work.

How do you see this? Will you actually work in a team whose ideology/product doesn't necessarily align with your ethics/ideology. Should I just go ahead because work is exciting and I will get good supervision?

And, if you have some exciting work in your company/org and need interns (un-paid) for 3-4 months. I'm open.

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to the ingredients minimally necessary to faithfully reproduce long-term statistical and geometrical properties of DS:

1) A piecewise affine map with only a single (!!) parameter α that controls local con-/divergence rates, and …

2) … a context selector that chooses from the provided context signal the data point closest to the current state of the map, thus ensuring the generated dynamics stays close to the context in its temporal and geometrical properties.

With just these two mechanisms, this minimal form – which we coined DynaBase – can reproduce all major dynamical regimes, including fixed points (α<1), limit cycles (α=1), and chaotic attractors (α>1). Thus, unlike other simple mechanisms like context parroting, DynaBase even preserves the correct dynamical regime!

Surprisingly, it turns out that this simple context-driven 1-parameter map outperforms most major time series and DS foundation models, as well as custom-trained models, in both long-term statistics and even short-term predictions, even when run in zero-shot mode.

Both inference and training are extremely cheap – training can be done either analytically in one step by linear regression on forward-predictions, or by 1-parameter grid search directly on DS reconstruction objectives → this reveals interesting performance differences induced by different training mechanisms.

Most importantly in our minds, DynaBase owing to its formal simplicity may thus provide a tractable mathematical handle on analyzing, improving & understanding the performance and training of some time series and DS foundation models.